End-to-end (E2E) solutions are attracting growing attention across a broad range of speech processing tasks [1]}, [2]}, [3]}. In contrast to the more common approach whereby front-end feature extraction and the back-end classifier or network are separately optimised, E2E solutions allow for pre-processing and post-processing components to be combined within a single network. With both components being encapsulated within a single model, front-end and back-end components can be jointly optimised. In this case the front-end might have a better chance of capturing more discriminative information for the task in hand [4]}, [5]}, [6]}, whereas the back-end might be able to function more effectively upon the information to produce more reliable scores.
